Search results for "python for generative ai"
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Python for Data Mining Quick Syntax Reference
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll LearnInstall Python and choose a development environmentUnderstand the basic concepts of object-oriented programmingImport, open, and edit filesReview the differences between Python 2.x and 3.xWho This Book Is ForProgrammers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
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Generative AI in Neurology
This book comprehensively explores the intersection between AI and neurology, focusing on the application of generative models in diagnosing, treating, and managing neurodegenerative diseases. It covers the principles of generative AI and its roles in neuroimaging, biomarker identification, medication development, and tailored treatment options. It examines how advanced AI approaches are leveraged to increase the detection and treatment of neurodegenerative illnesses. The concepts are supported by case studies and examples.Features:Focuses on the emerging field of generative models in neurology.Presents an interdisciplinary approach that bridges AI and medical applications.Examines the ability of neural diffusion models to generate genuine data to enhance healthcare AI.Discusses tools for the adaptation and augmentation of medical images and healthcare data domains.Includes case studies and practical impacts.This book is aimed at graduate students and researchers in biomedical and computer engineering, artificial intelligence, and health informatics.
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Derivatives Analytics with Python
Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourselfApply Fourier transform techniques and advanced Monte Carlo pricingCalibrate advanced option pricing models to market dataIntegrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.
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Generative AI with SAP and Amazon Bedrock
Explore Generative AI and understand its key concepts, architecture, and tangible business use cases. This book will help you develop the skills needed to use SAP AI Core service features available in the SAP Business Technology Platform.You’ll examine large language model (LLM) concepts and gain the practical knowledge to unleash the best use of Gen AI. As you progress, you’ll learn how to get started with your own LLM models and work with Generative AI use cases. Additionally, you’ll see how to take advantage Amazon Bedrock stack using AWS SDK for ABAP.To fully leverage your knowledge, Generative AI with SAP and Amazon Bedrock offers practical step-by-step instructions for how to establish a cloud SAP BTP account model and create your first GenAIartifacts. This work is an important prerequisite for those who want to take full advantage of generative AI with SAP.What You Will LearnMaster the concepts and terminology of artificial intelligence and GenAIUnderstand opportunities and impacts for different industries with GenAIBecome familiar with SAP AI Core, Amazon Bedrock, AWS SDK for ABAP and develop your firsts GenAI projectsAccelerate your development skillsGain more productivity and time implementing GenAI use casesWho this Book Is ForAnyone who wants to learn about Generative AI for Enterprise and SAP practitioners who want to take advantage of AI within the SAP ecosystem to support their systems and workflows.
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